E-Book Overview
Although stochastic kinetic models are increasingly accepted as the best way to represent and simulate genetic and biochemical networks, most researchers in the field have limited knowledge of stochastic process theory. The stochastic processes formalism provides a beautiful, elegant, and coherent foundation for chemical kinetics and there is a wealth of associated theory every bit as powerful and elegant as that for conventional continuous deterministic models. The time is right for an introductory text written from this perspective. Stochastic Modelling for Systems Biology presents an accessible introduction to stochastic modelling using examples that are familiar to systems biology researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modelling biological systems. He provides a comprehensive understanding of stochastic kinetic modelling of biological networks in the systems biology context. The text covers the latest simulation techniques and research material, such as parameter inference, and includes many examples and figures as well as software code in R for various applications.While emphasizing the necessary probabilistic and stochastic methods, the author takes a practical approach, rooting his theoretical development in discussions of the intended application. Written with self-study in mind, the book includes technical chapters that deal with the difficult problems of inference for stochastic kinetic models from experimental data. Providing enough background information to make the subject accessible to the non-specialist, the book integrates a fairly diverse literature into a single convenient and notationally consistent source.
E-Book Content
Chapman & Hall/CRC Mathematical and Computational Biology Series Stochastic Modelling for Systems Biology Published Titles Differential Equations and Mathematical Biology, D.S. Jones and B.D. Sleeman Modeling and Simulation of Capsules and Biological Cells, C. Pozrikidis Cancer Modelling and Simulation, Luigi Preziosi Data Analysis Tools for DNA Microarrays, Sorin Draghici Computational Neuroscience: A Comprehensive Approach, Jianfeng Feng The Ten Most Wanted Solutions in Protein Bioinformatics, Anna Tramontano Exactly Solvable Models of Biological Invasion, Sergei V. Petrovskii and Lian-Bai Li Knowledge Discovery in Proteomics, Igor Jurisica and Dennis Wigle Normal Mode Analysis: Theory and Applications to Biological and Chemical Systems, Qiang Cui and /vet Bahar An Introduction to Systems Biology: Design Principles of Biological Circuits, Uri A/on Stochastic Modelling for Systems Biology, James Darren Wilkinson Forthcoming Titles Computational Biology: A Statistical Mechanics Perspective, Raft 8/ossey Introduction to Bioinformatics, Anna Tramontano Biological Sequence Analysis with Iterative Maps, Jonas S. Almeida Practical Guide to Protein Bioinformatics: Sequence, Structure and Function, Shoba Ranganathan CHAPMAN & HALUCRC Mathematical and Computational Biology Series Aims and scope: This series aims to capture new developments and summarize what is known over the whole spectrum of mathematical and computational biology and medicine. It seeks to encourage the integration of mathematical, statistical and computational methods into biology by publishing a broad range of textbooks, reference works and handbooks. The titles included in the series are meant to appeal to students, researchers and professionals in the mathematical, statistical and computational sciences, fundamental biology and bioengineering, as well as interdisciplinary researchers involved in the field. The inclusion of concrete examples and applications, and programming techniques and examples, is highly encouraged. Series Editors Alison M. Etheridge Department of Statistics University of Oxford Louis J. Gross D